Numpy Introduction – Python Tutorials

Welcome back friends in our new Python’s Numpy Introduction tutorial. In this tutorial, we will simply discuss what is Numpy, Where we should use it and etc.

Lets get explore The Numpy Introduction...

What is Numpy?

Numpy is nothing but a Python package. It was developed by Travis Oliphant in 2005. It is an open-source project and you can use it freely. Some people write or pronounce it as a linear algebra library for Python. And the reason it’s so important for data science with Python is that almost all of the other libraries. Python’s Numpy refers to NUMERICAL PYTHON.

If you are working on jupyter notebook you don’t need to install it. For others you have to install by below-mentioned command:

conda install numpy

Why We Use NumPy?

Numpy is mainly used for python multidimension array. It is open source package, we can use it freely. if we talk about arrays Python has list for that, so the is why we use numpy? yes, we are using numpy because it aims to provide an array object, up to 50x faster than the Python lists.

Why NumPy is faster than Python List? NumPy arrays are stored at one place in memory unlike Python lists, so processes can access and manipulate them very efficiently.

Numpy Introduction

Numpy Array Object: In NumPy is called ndarray, it provides so many supporting functions which make working with ndarray very easy.

This is all about the Numpy Introduction part in Python Tutorials. If you want documentation on Python’s Numpy click here which redirects you to the Numpy’s site.


Having undergone major version changes, which is the current (and the latest) version of a 64-bit Python as of December 2020?

 A. 3.8.6
 B. 3.9.1
C. 3.5.10
 D. 3.7.9


Which of the following is NOT an acceptable naming convention for variables in Python?

A. Age=57
C. 2age=57
D. age=57


What are the values of the following expressions respectively: (i) 100 / 25, (ii) 100 / / 25?

A. 4, 4.0
 B. 4.0, 4
 C. 4, 4
D. 4.0, 4.0


Compute the value of the expression: 22 + 2**5 * 192 / 48 – 100
A. 6.0
B. 50.0
C. 116.0
D. -42.0


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